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2. Feasibility and Accessibility of Human-Centered AI-Based Simulation System for Improving the Occupational Safety of Clinical Workplace

Author

Wang, Pin-Hsuan, Huang, Anna YuQing, Huang, Yen-Hsun, Yang, Ying-Ying, Lirng, JiingFeng, Li, Tzu-Hao, Hou, Ming-Chih, Chen, Chen-Huan, Yang, Albert ChihChieh, Lin, Chi-Hung, and Sheu, Wayne Huey-Herng

Abstract

Medical personnel need to learn occupational safety knowledge in clinical workplaces, not only to ensure their own safety, but also to further ensure patients safety. Based on Human-centered artificial intelligence (HAI) technology, this study will provide HAI-based occupational safety training system for two training topics, Needle Stick/Sharps Injury (NSSI) prevention and appropriate Clinical Waste Management (CWM). From April 2018 to December 2021, this clinical occupational safety HAI training is used by 342 medical personnel (doctors and non-doctors). This study aims to investigate the learning performance and effectiveness including decreasing anxiety and increasing mastering level of users. This study shows that, for the first-time and feel-friendly users of this HAI training system, not only can they achieve significant learning improvement, but they can also effectively decrease their anxiety and increase their mastery level of clinical work safety knowledge and skill. In terms of learning performance and effectiveness, this study found that doctors are significantly benefited by the HAI training system in contrast to non-doctors.

Published
2023

3. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L. T., Kreienkamp, Raymond J., Lim, Lee-Ling, Männistö, Jonna M. E., Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K. W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, Jr, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S. F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey-Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie-France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J. F., Ma, Ronald C. W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Published
2023
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4. Data from network meta-analyses can inform clinical practice guidelines and decision-making in diabetes management: perspectives of the taskforce of the guideline workshop

Author

Ceriello, Antonio, Rodbard, Helena W., Battelino, Tadej, Brosius, Frank, Cosentino, Francesco, Green, Jennifer, Ji, Linong, Kellerer, Monika, Koob, Susan, Kosiborod, Mikhail, Lalic, Nebojsa, Marx, Nikolaus, Nedungadi, T. Prashant, Parkin, Christopher G., Rydén, Lars, Sheu, Wayne Huey-Herng, Standl, Eberhard, Vandvik, Per Olav, and Schnell, Oliver

Published
2023
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5. Precision subclassification of type 2 diabetes: a systematic review

Author

Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C., Kreienkamp, Raymond J., Cromer, Sara J., Rooney, Mary R., Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S., Leong, Aaron, Deutsch, Aaron J., Andersen, Mette K., Billings, Liana K., Eckel, Robert H., Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O., Ray, Debashree, Selvin, Elizabeth, Florez, Jose C., Meigs, James B., and Udler, Miriam S.

Published
2023
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12. Defining patient-reported outcomes in diabetes, obesity, cardiovascular disease, and chronic kidney disease for clinical practice guidelines - perspectives of the taskforce of the Guideline Workshop.

Author

Barnard-Kelly, Katharine, Battelino, Tadej, Brosius, Frank C., Ceriello, Antonio, Cosentino, Francesco, Gavin III, James R., Giorgino, Francesco, Green, Jennifer, Ji, Linong, Kellerer, Monika, Koob, Sue, Kosiborod, Mikhail, Lalic, Nebojsa, Marx, Nikolaus, Nedungadi, Prashant, Parkin, Christopher G., Rodbard, Helena W., Rötzer, René D., Rydén, Lars, and Sheu, Wayne Huey-Herng

Subjects
CHRONIC kidney failure, QUALITY of life, MEDICAL sciences, CARDIOVASCULAR diseases, PUBLIC health
Abstract

Recent clinical practice guidelines for diabetes, obesity, cardiovascular disease (CVD) and chronic kidney disease (CKD) emphasise a holistic, person-centred approach to care. However, they do not include recommendations for the assessment of patient-reported outcomes (PROs), which would – dependent on the topic of guideline – be important for improving shared decision-making, patients' concordance with guideline recommendations, clinical outcomes and health-related quality of life (HRQoL). The Taskforce of the Guideline Workshop discussed PROs in diabetes, obesity, CVD and CKD as well as the relevance of their inclusion in clinical practice guidelines for the management of these conditions. Highlights: Patient engagement in disease management is a topic that is becoming increasingly important, also in terms of improving clinical outcomes Person-reported outcome measures (PROMs) are tools to assess person-reported outcomes (PROs) in an efficient and standardized manner Validated disease-specific measures for diabetes, obesity, CVD, and CKD as well as generic measures are available Currently underutilised in the routine care of cardio-renal-metabolic diseases, the integration of recommendations on PROs and PROMs into clinical practice guidelines may help to overcome the barriers to their implementation [ABSTRACT FROM AUTHOR]

Published
2025
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14. Genetic variation of SORBS1 gene is associated with glucose homeostasis and age at onset of diabetes: A SAPPHIRe Cohort Study

Author

Chang, Tien-Jyun, Wang, Wen-Chang, Hsiung, Chao A, He, Chih-Tsueng, Lin, Ming-Wei, Sheu, Wayne Huey-Herng, Chang, Yi-Cheng, Quertermous, Tom, Chen, Yii-Der Ida, Rotter, Jerome I, Chuang, Lee-Ming, and The SAPPHIRe Study Group

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Nutrition, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Age of Onset, Asian People, Blood Glucose, China, Diabetes Mellitus, Type 2, Female, Follow-Up Studies, Genetic Predisposition to Disease, Glucose Tolerance Test, Humans, Incidence, Insulin, Male, Microfilament Proteins, Middle Aged, Polymorphism, Single Nucleotide, Prevalence, SAPPHIRe Study Group
Abstract

The SORBS1 gene plays an important role in insulin signaling. We aimed to examine whether common single-nucleotide polymorphisms (SNPs) of SORBS1 are associated with prevalence and incidence of diabetes, age at onset of diabetes, and the related traits of glucose homeostasis. A total of 1135 siblings from 492 ethnic Chinese families were recruited at baseline, and 630 were followed up for 5.19 ± 0.96 years. Nine SNPs including rs7081076, rs2281939, rs3818540, rs2274490, rs61739184, rs726176, rs2296966, rs17849148, and rs3193970 were genotyped and examined. To deal with correlated data of subjects within the same families, the generalized estimating equations approach was applied throughout all association analyses. The GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test. These two SNPs revealed independent associations with age of diabetes onset as well as risk of diabetes at baseline. These findings supported that SORBS1 gene participates in the pathogenesis of diabetes.

Published
2018

17. Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Healthcare Innovation: A 25-Year Perspective with a Focus on Privacy and Privacy-Enhancing Technologies (Preprint)

Author

Yang, Albert, primary, Pan, Mei-Lien, additional, Lu, Henry Horng-Shing, additional, Lien, Chung-Yueh, additional, Wang, Da-Wei, additional, Chen, Chih-Hsiung, additional, Tarng, Der-Cherng, additional, Niu, Dau-Ming, additional, Chiou, Shih-Hwa, additional, Wu, Chun-Ying, additional, Sun, Ying - Chou, additional, Chen, Shih-Ann, additional, Wang, Shuu-Jiun, additional, Sheu, Wayne Huey-Herng, additional, and Lin, Chi-Hung, additional

Published
2024
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24. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Author

Wen, Wanqing, Zheng, Wei, Okada, Yukinori, Takeuchi, Fumihiko, Tabara, Yasuharu, Hwang, Joo-Yeon, Dorajoo, Rajkumar, Li, Huaixing, Tsai, Fuu-Jen, Yang, Xiaobo, He, Jiang, Wu, Ying, He, Meian, Zhang, Yi, Liang, Jun, Guo, Xiuqing, Sheu, Wayne Huey-Herng, Delahanty, Ryan, Guo, Xingyi, Kubo, Michiaki, Yamamoto, Ken, Ohkubo, Takayoshi, Go, Min Jin, Liu, Jian Jun, Gan, Wei, Chen, Ching-Chu, Gao, Yong, Li, Shengxu, Lee, Nanette R, Wu, Chen, Zhou, Xueya, Song, Huaidong, Yao, Jie, Lee, I-Te, Long, Jirong, Tsunoda, Tatsuhiko, Akiyama, Koichi, Takashima, Naoyuki, Cho, Yoon Shin, Ong, Rick TH, Lu, Ling, Chen, Chien-Hsiun, Tan, Aihua, Rice, Treva K, Adair, Linda S, Gui, Lixuan, Allison, Matthew, Lee, Wen-Jane, Cai, Qiuyin, Isomura, Minoru, Umemura, Satoshi, Kim, Young Jin, Seielstad, Mark, Hixson, James, Xiang, Yong-Bing, Isono, Masato, Kim, Bong-Jo, Sim, Xueling, Lu, Wei, Nabika, Toru, Lee, Juyoung, Lim, Wei-Yen, Gao, Yu-Tang, Takayanagi, Ryoichi, Kang, Dae-Hee, Wong, Tien Yin, Hsiung, Chao Agnes, Wu, I-Chien, Juang, Jyh-Ming Jimmy, Shi, Jiajun, Choi, Bo Youl, Aung, Tin, Hu, Frank, Kim, Mi Kyung, Lim, Wei Yen, Wang, Tzung-Dao, Shin, Min-Ho, Lee, Jeannette, Ji, Bu-Tian, Lee, Young-Hoon, Young, Terri L, Shin, Dong Hoon, Chun, Byung-Yeol, Cho, Myeong-Chan, Han, Bok-Ghee, Hwu, Chii-Min, Assimes, Themistocles L, Absher, Devin, Yan, Xiaofei, Kim, Eric, Kuo, Jane Z, Kwon, Soonil, Taylor, Kent D, Chen, Yii-Der I, Rotter, Jerome I, Qi, Lu, Zhu, Dingliang, Wu, Tangchun, Mohlke, Karen L, and Gu, Dongfeng

Subjects
Biological Sciences, Genetics, Obesity, Human Genome, Nutrition, Women's Health, 1.1 Normal biological development and functioning, Stroke, 5'-Nucleotidase, Aldehyde Dehydrogenase, Aldehyde Dehydrogenase, Mitochondrial, Asian People, Blood Proteins, Body Mass Index, Cardiac Myosins, Asia, Eastern, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glycoproteins, Humans, KCNQ1 Potassium Channel, Male, Myosin Light Chains, Polymorphism, Single Nucleotide, Proteinase Inhibitory Proteins, Secretory, Medical and Health Sciences, Genetics & Heredity
Abstract

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488-47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10(-13)), ALDH2/MYL2 (rs671, P = 3.40 × 10(-11); rs12229654, P = 4.56 × 10(-9)), ITIH4 (rs2535633, P = 1.77 × 10(-10)) and NT5C2 (rs11191580, P = 3.83 × 10(-8)) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10(-8)) and an additional 14 at P < 1.0 × 10(-3) with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity.

Published
2014

25. Optimizing lipid control in Taiwanese diabetic patients: A collaborative consensus by the Diabetes Association of the Republic of China (Taiwan) and the Taiwanese Association of Diabetes Educators.

Author

Shen, Feng‐Chih, Chu, Chih‐Hsun, Chen, Jung‐Fu, Kuo, Chin‐Sung, Hsu, Chih‐Yao, Lin, Ching‐Han, Sheen, Yi‐Jing, Su, Sheng‐Chiang, Tien, Kai‐Jen, Lu, Chieh‐Hua, Lee, Chun‐Chuan, Yang, Yi‐Sun, Tu, Shih‐Te, Chen, Po‐Tsang, Chen, Ching‐Chu, Chien, Ming‐Nan, Li, Hung‐Yuan, Sheu, Wayne Huey‐Herng, Huang, Chien‐Ning, and Wang, Chih‐Yuan

Subjects
CONSENSUS (Social sciences), GLYCEMIC control, DIABETES, PEOPLE with diabetes, HYPERLIPIDEMIA
Abstract

We present an in‐depth analysis of dyslipidemia management strategies for patients with diabetes mellitus in Taiwan. It critically examines the disparity between established guideline recommendations and actual clinical practices, particularly in the context of evolving policies affecting statin prescriptions. The focus is on synthesizing the most recent findings concerning lipid management in patients with diabetes mellitus, with a special emphasis on establishing consensus regarding low‐density lipoprotein cholesterol treatment targets. The article culminates in providing comprehensive, evidence‐based recommendations tailored to the unique needs of those living with diabetes mellitus in Taiwan. It underscores the criticality of personalized care approaches, which incorporate multifaceted factors, and the integration of novel therapeutic options to enhance cardiovascular health outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Author

Kraja, Aldi T., Liu, Chunyu, Fetterman, Jessica L., Graff, Mariaelisa, Have, Christian Theil, Gu, Charles, Yanek, Lisa R., Feitosa, Mary F., Arking, Dan E., Chasman, Daniel I., Young, Kristin, Ligthart, Symen, Hill, W. David, Weiss, Stefan, Luan, Jian’an, Giulianini, Franco, Li-Gao, Ruifang, Hartwig, Fernando P., Lin, Shiow J., Wang, Lihua, Richardson, Tom G., Yao, Jie, Fernandez, Eliana P., Ghanbari, Mohsen, Wojczynski, Mary K., Lee, Wen-Jane, Argos, Maria, Armasu, Sebastian M., Barve, Ruteja A., Ryan, Kathleen A., An, Ping, Baranski, Thomas J., Bielinski, Suzette J., Bowden, Donald W., Broeckel, Ulrich, Christensen, Kaare, Chu, Audrey Y., Corley, Janie, Cox, Simon R., Uitterlinden, Andre G., Rivadeneira, Fernando, Cropp, Cheryl D., Daw, E. Warwick, van Heemst, Diana, de las Fuentes, Lisa, Gao, He, Tzoulaki, Ioanna, Ahluwalia, Tarunveer S., de Mutsert, Renée, Emery, Leslie S., Erzurumluoglu, A. Mesut, Perry, James A., Fu, Mao, Forouhi, Nita G., Gu, Zhenglong, Hai, Yang, Harris, Sarah E., Hemani, Gibran, Hunt, Steven C., Irvin, Marguerite R., Jonsson, Anna E., Justice, Anne E., Kerrison, Nicola D., Larson, Nicholas B., Lin, Keng-Hung, Love-Gregory, Latisha D., Mathias, Rasika A., Lee, Joseph H., Nauck, Matthias, Noordam, Raymond, Ong, Ken K., Pankow, James, Patki, Amit, Pattie, Alison, Petersmann, Astrid, Qi, Qibin, Ribel-Madsen, Rasmus, Rohde, Rebecca, Sandow, Kevin, Schnurr, Theresia M., Sofer, Tamar, Starr, John M., Taylor, Adele M., Teumer, Alexander, Timpson, Nicholas J., de Haan, Hugoline G., Wang, Yujie, Weeke, Peter E., Williams, Christine, Wu, Hongsheng, Yang, Wei, Zeng, Donglin, Witte, Daniel R., Weir, Bruce S., Wareham, Nicholas J., Vestergaard, Henrik, Turner, Stephen T., Torp-Pedersen, Christian, Stergiakouli, Evie, Sheu, Wayne Huey-Herng, Rosendaal, Frits R., Ikram, M. Arfan, Franco, Oscar H., Ridker, Paul M., Perls, Thomas T., Pedersen, Oluf, Nohr, Ellen A., Newman, Anne B., Linneberg, Allan, Langenberg, Claudia, Kilpeläinen, Tuomas O., Kardia, Sharon L.R., Jørgensen, Marit E., Jørgensen, Torben, Sørensen, Thorkild I.A., Homuth, Georg, Hansen, Torben, Goodarzi, Mark O., Deary, Ian J., Christensen, Cramer, Chen, Yii-Der Ida, Chakravarti, Aravinda, Brandslund, Ivan, Bonnelykke, Klaus, Taylor, Kent D., Wilson, James G., Rodriguez, Santiago, Davies, Gail, Horta, Bernardo L., Thyagarajan, Bharat, Rao, D.C., Grarup, Niels, Davila-Roman, Victor G., Hudson, Gavin, Guo, Xiuqing, Arnett, Donna K., Hayward, Caroline, Vaidya, Dhananjay, Mook-Kanamori, Dennis O., Tiwari, Hemant K., Levy, Daniel, Loos, Ruth J.F., Dehghan, Abbas, Elliott, Paul, Malik, Afshan N., Scott, Robert A., Becker, Diane M., de Andrade, Mariza, Province, Michael A., Meigs, James B., Rotter, Jerome I., and North, Kari E.

Published
2019
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29. 2018 consensus of the Taiwan Society of Cardiology and the Diabetes Association of Republic of China (Taiwan) on the pharmacological management of patients with type 2 diabetes and cardiovascular diseases

Author

Chiang, Chern-En, Lin, Shih-Yi, Lin, Tsung-Hsien, Wang, Tzung-Dau, Yeh, Hung-I, Chen, Jung-Fu, Tsai, Chia-Ti, Hung, Yi-Jen, Li, Yi-Heng, Liu, Ping-Yen, Chang, Kuan-Cheng, Wang, Kang-Ling, Chao, Ting-Hsing, Shyu, Kou-Gi, Yang, Wei-Shiung, Ueng, Kwo-Chang, Chu, Pao-Hsien, Yin, Wei-Hsian, Wu, Yen-Wen, Cheng, Hao-Min, Shin, Shyi-Jang, Huang, Chien-Ning, Chuang, Lee-Ming, Lin, Shing-Jong, Yeh, San-Jou, Sheu, Wayne Huey-Herng, and Lin, Jiunn-Lee

Published
2018
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31. Trans-ethnic fine mapping identifies a novel independent locus at the 3′ end of CDKAL1 and novel variants of several susceptibility loci for type 2 diabetes in a Han Chinese population

Author

Kuo, Jane Z, Sheu, Wayne Huey-Herng, Assimes, Themistocles L, Hung, Yi-Jen, Absher, Devin, Chiu, Yen-Feng, Mak, Jordan, Wang, Jun-Sing, Kwon, Soonil, Hsu, Chih-Cheng, Goodarzi, Mark O, Lee, I-Te, Knowles, Joshua W, Miller, Brittany E, Lee, Wen-Jane, Juang, Jyh-Ming J, Wang, Tzung-Dau, Guo, Xiuqing, Taylor, Kent D, Chuang, Lee-Ming, Hsiung, Chao A, Quertermous, Thomas, Rotter, Jerome I, and Chen, Yii-Der I

Subjects
Epidemiology, Health Sciences, Prevention, Minority Health, Human Genome, Diabetes, Autoimmune Disease, Genetics, Nutrition, Health Disparities, 2.1 Biological and endogenous factors, Metabolic and endocrine, Aged, Asian People, Blood Glucose, Cyclin-Dependent Kinase 5, DNA Mutational Analysis, Diabetes Mellitus, Type 2, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotyping Techniques, Humans, Insulin, Male, Polymorphism, Single Nucleotide, White People, tRNA Methyltransferases, Ethnic difference, Genetic association, Type 2 diabetes, Clinical Sciences, Paediatrics and Reproductive Medicine, Public Health and Health Services, Endocrinology & Metabolism, Clinical sciences, Public health
Abstract

Aims/hypothesisCandidate gene and genome-wide association studies have identified ∼60 susceptibility loci for type 2 diabetes. A majority of these loci have been discovered and tested only in European populations. The aim of this study was to assess the presence and extent of trans-ethnic effects of these loci in an East Asian population.MethodsA total of 9,335 unrelated Chinese Han individuals, including 4,535 with type 2 diabetes and 4,800 non-diabetic ethnically matched controls, were genotyped using the Illumina 200K Metabochip. We tested 50 established loci for type 2 diabetes and related traits (fasting glucose, fasting insulin, 2 h glucose). Disease association with the additive model of inheritance was analysed with logistic regression.ResultsWe found that 14 loci significantly transferred to the Chinese population, with two loci (p = 5.7 × 10(-12) for KCNQ1; p = 5.0 × 10(-8) for CDKN2A/B-CDKN2BAS) reaching independent genome-wide statistical significance. Five of these 14 loci had similar lead single-nucleotide polymorphisms (SNPs) as were found in the European studies while the other nine were different. Further stepwise conditional analysis identified a total of seven secondary signals and an independent novel locus at the 3' end of CDKAL1.Conclusions/interpretationThese results suggest that many loci associated with type 2 diabetes are commonly shared between European and Chinese populations. Identification of population-specific SNPs may increase our understanding of the genetic architecture underlying type 2 diabetes in different ethnic populations.

Published
2013

33. Use of Facebook by Academic Medical Centers in Taiwan During the COVID-19 Pandemic: Observational Study

Author

Chu, Wei-Min, Shieh, Gow-Jen, Wu, Shi-Liang, and Sheu, Wayne Huey-Herng

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe battle against COVID-19 remains ongoing, and social media has played an important role during the crisis for both communication and health promotion, particularly for health care organizations. Taiwan’s success during the COVID-19 outbreak is well known and the use of social media is one of the key contributing factors to that success. ObjectiveThis nationwide observational study in Taiwan aimed to explore the use of Facebook by academic medical centers during the COVID-19 pandemic. MethodsWe conducted a nationwide observational study of all Facebook fan page posts culled from the official accounts of all medical centers in Taiwan from December 2019 to April 2020. All Facebook posts were categorized into either COVID-19–related posts or non–COVID-19–related posts. COVID-19–related posts were split into 4 categories: policy of Taiwan’s Center for Disease Control (TCDC), gratitude notes, news and regulations from hospitals, and education. Data from each post was also recorded as follows: date of post, headline, number of “likes,” number of messages left, number of shares, video or non-video post, and date of search. ResultsThe Facebook fan pages of 13 academic medical centers, with a total of 1816 posts, were analyzed. From January 2020, the percentage of COVID-19 posts increased rapidly, from 21% (January 2020) to 56.3% (April 2020). The trends of cumulative COVID-19 posts and reported confirmed cases were significantly related (Pearson correlation coefficient=0.93, P

Published
2020
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40. Treatment goal achievement and association with 10-year and 30-year atherosclerotic cardiovascular disease risks in patients with type 1 diabetes mellitus in Taiwan: The Taiwan Diabetes Registry

Author

Lin, Hsuan-Wei, primary, Yeh, Yun-Kai, additional, Wu, Tsung-Hui, additional, Sheu, Wayne Huey-Herng, additional, Lo, Su-Huey, additional, Yeh, Yen-Po, additional, Huang, Chien-Ning, additional, Hsieh, Chang-Hsun, additional, and Hwu, Chii-Min, additional

Published
2023
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41. A risk stratification model modified from the U.S. guideline could be applied in an Asian population with or without ASCVD: Validation study

Author

Hsiao, Yu-Chung, primary, Lee, Thung-Lip, additional, Lin, Fang-Ju, additional, Hsuan, Chin-Feng, additional, Yeh, Chih-Fan, additional, Chang, Wei-Tien, additional, Kao, Hsien-Li, additional, Jeng, Jiann-Shing, additional, Wu, Yen-Wen, additional, Hsieh, I-Chang, additional, Fang, Ching-Chang, additional, Wang, Kuo-Yang, additional, Chang, Kuan-Cheng, additional, Lin, Tsung-Hsien, additional, Sheu, Wayne Huey-Herng, additional, Li, Yi-Heng, additional, Yin, Wei-Hsian, additional, Yeh, Hung-I, additional, Chen, Jaw-Wen, additional, and Wu, Chau-Chung, additional

Published
2023
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42. A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization

Author

Smith, Johanna L., primary, Tcheandjieu, Catherine, additional, Dikilitas, Ozan, additional, Iyer, Kruthika, additional, Miyazawa, Kazuo, additional, Hilliard, Austin, additional, Lynch, Julie, additional, Rotter, Jerome I., additional, Chen, Yii-Der Ida, additional, Sheu, Wayne Huey-Herng, additional, Chang, Kyong-Mi, additional, Kanoni, Stavroula, additional, Tsao, Philip, additional, Ito, Kaoru, additional, Kosel, Matthew, additional, Clarke, Shoa L., additional, Schaid, Daniel J., additional, Assimes, Themistocles L., additional, and Kullo, Iftikhar J., additional

Published
2023
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45. Precision subclassification of type 2 diabetes:a systematic review

Author

Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C, Kreienkamp, Raymond J, Cromer, Sara J, Rooney, Mary R, Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S, Leong, Aaron, Deutsch, Aaron J, Andersen, Mette K, Billings, Liana K, Eckel, Robert H, Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O, Ray, Debashree, Selvin, Elizabeth, Florez, Jose C, Meigs, James B, Udler, Miriam S, Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C, Kreienkamp, Raymond J, Cromer, Sara J, Rooney, Mary R, Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S, Leong, Aaron, Deutsch, Aaron J, Andersen, Mette K, Billings, Liana K, Eckel, Robert H, Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O, Ray, Debashree, Selvin, Elizabeth, Florez, Jose C, Meigs, James B, and Udler, Miriam S

Abstract

Background Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. Methods We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. Results Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. Conclusion Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes., BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Published
2023

47. Systematic review of precision subclassification of type 2 diabetes

Author

Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C., Kreienkamp, Raymond J., Cromer, Sara J., Rooney, Mary R., Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S., Leong, Aaron, Deutsch, Aaron J., Andersen, Mette K., Billings, Liana K., Eckel, Robert H, Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O., Ray, Debashree, Selvin, Elizabeth, Florez, Jose C., Meigs, James B., and Udler, Miriam S.

Subjects
Article
Abstract

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed ‘simple subclassification’ using clinical features, biomarkers, imaging or other routinely available parameters or ‘complex subclassification’ approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Published
2023

48. Additional file 1 of Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

Author

Ou, Shuo-Ming, Tsai, Ming-Tsun, Lee, Kuo-Hua, Tseng, Wei-Cheng, Yang, Chih-Yu, Chen, Tz-Heng, Bin, Pin-Jie, Chen, Tzeng-Ji, Lin, Yao-Ping, Sheu, Wayne Huey-Herng, Chu, Yuan-Chia, and Tarng, Der-Cherng

Abstract

Additional file 1: Supplementary Table 1. The performance of machine learning models after data augmentation for predicting the risk of end-stage renal disease in newly diagnosed type 2 diabetes mellitus. Supplementary Figure 1. Area under the receiver operating characteristic curve for the 5-fold cross-validation of the XGBoost machine learning models.

Published
2023
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49. Additional file 1 of Unhealthy lifestyle associated with increased risk of macro- and micro-vascular comorbidities in patients with long-duration type 2 diabetes: results from the Taiwan Diabetes Registry

Author

Ho, Li-Ju, Sheu, Wayne Huey-Herng, Lo, Su-Huey, Yeh, Yen-Po, Hwu, Chii-Min, Huang, Chien-Ning, Hsieh, Chang-Hsun, and Kuo, Feng-Chih

Abstract

Additional file 1. Table S1. Prevalence of factors that stand for unhealthy lifestyle among long-duration and newly diagnosed T2DM patients. Table S2. Odds ratios for cardiovascular disease, PAOD, and nephropathy divided by number of factors in patients with newly diagnosed T2DM.

Published
2023
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